How do I know if AI output is good enough to use?

What it is

This is the practical side of evals. It applies to any repeated AI task: support replies, report summaries, product descriptions.

How it works

1. Write success criteria a colleague could check without asking you.
2. Gather real examples, including tricky ones. Anthropic suggests edge cases such as missing or irrelevant input, overly long input and ambiguous cases.
3. Score the outputs by rule (format, required fields), with a grader model or by a person. Anthropic recommends automating grading where you can, and favoring more test cases over fewer hand-graded ones.
4. Set a threshold for going live and a process for failures.

How many examples? IonBin suggests 20 to 50 real examples to start. That’s our guidance, not a vendor figure.

Benefits

• Replaces gut feel with evidence.
• Lets you compare vendors fairly (see which model).
• Catches silent regressions after vendor updates.

Limits and risks

• Small test sets miss rare failures.
• Grader models can be biased toward certain styles, so spot-check them.
• Christopher Penn argues that generic benchmarks tell you little about your own tasks (his video). That’s a practitioner view.